Everyone interested in this topic is welcomed to join us. the following is the abstract for this week\u2019s paper.<\/p>\n\n\n\n

\n\t\t

\n\t\t\tEnd-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF<\/span>\n\t\t<\/p>\n\t\t

\n\t\t\tXuezhe Ma and Eduard Hovy<\/span>\n\t\t<\/p>\n\t\t

\n\t\t\tAbstract<\/span>\n\t\t<\/p>\n\t\t

\n\t\t\tState-of-the-art sequence labeling\nsystems traditionally require large amounts of task-specific knowledge in the\nform of handcrafted features and data pre-processing. In this paper, we\nintroduce a novel neutral network architecture that benefits from both word-\nand character-level representations automatically, by using combination of\nbidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no\nfeature engineering or data preprocessing, thus making it applicable to a wide\nrange of sequence labeling tasks. We evaluate our system on two data sets for\ntwo sequence labeling tasks \u2014 Penn Treebank WSJ corpus for part-of-speech (POS)\ntagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain\nstate-of-the-art performance on both datasets\u201497.55% accuracy for POS tagging\nand 91.21% F1 for NER.<\/span>\n\t\t<\/p>\n\t<\/div>\n<\/p>\n\n\n\n

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF<\/a>\u4e0b\u8f7d<\/a><\/div>\n\n\n\n\n\n\n\n

NLPIR SEMINAR 24th ISSUE COMPLETED<\/strong><\/h2>\n\n\n\n
Last Monday, Zhaoyang Wang<\/strong> gave a presentation about the paper, End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF<\/strong>, and shared some opinion on it.<\/p>\n\n\n\n
$\"\"$ <\/figure>\n\n\n\n
In the picture, dashed arrows indicate dropout layers applied on both the input and output vectors of BLSTM. One is that a dropout layer applied before character embeddings are input to CNN. Another is that dropout layers are applied on both the input and output vectors of BLSTM. The dropout layers can reduce overfitting. <\/p>\n\n\n\n
The LSTM-CNN-CRF model can also be applied to ancient Chinese sentence tagging.<\/p>\n","protected":false},"excerpt":{"rendered":"
NLPIR SEMINAR Y2019#11 INTRO In the new … \u7ee7\u7eed\u9605\u8bfb →<\/span><\/a><\/p>\n","protected":false},"author":862,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37,38],"tags":[],"_links":{"self":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6864"}],"collection":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/users\/862"}],"replies":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/comments?post=6864"}],"version-history":[{"count":5,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6864\/revisions"}],"predecessor-version":[{"id":6899,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/6864\/revisions\/6899"}],"wp:attachment":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/media?parent=6864"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/categories?post=6864"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/tags?post=6864"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

INTRO <\/h3>\n\n\n\n In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n

INTRO <\/h3>\n\n\n\n
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.
<\/p>\n\n\n\n